In many 3D stereoscopic applications, so called disparity maps are calculated on the basis of two views supplied by two cameras, for example. A disparity map contains relative depth information gained by comparing two images. A disparity map is often necessary for specific processing steps used to improve the images presented to a user. One of the steps to calculate a disparity map is disparity estimation which can be seen as a correspondence search between two or more images, for example left and right images. Since each image comprises generally three channels, namely RGB or YUV channels, the correspondence search uses information from all channels of the left and right images. However, this requires complex hardware, e.g. large memory for storing all channels, and is time consuming. For cost reasons, there are systems using only one channel, typically the luminance (Y) channel of the images for the correspondence search.
Although the disparity estimation on the basis of a singlechannel, for example the luminance (Y) channel, works well in practice, there is a demand to further improve the disparity estimation step.